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Neural networks for modelling and control of a non-linear dynamic system

Murray-Smith, R., Neumerkel, D., and Sbarbaro-Hofer, D. (1992) Neural networks for modelling and control of a non-linear dynamic system. In: IEEE International Symposium on Intelligent Control, 11-13 August 1992, Glasgow, Scotland.

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Publisher's URL: http://dx.doi.org/10.1109/ISIC.1992.225125

Abstract

The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved in the transfer of connectionist theory to practice are discussed.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Prof Roderick
Authors: Murray-Smith, R., Neumerkel, D., and Sbarbaro-Hofer, D.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Computing Science
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Copyright Holders:Copyright © 1992 Institute of Electrical and Electronics Engineers (IEEE)
First Published:First published in Proceedings of the 1992 IEEE International Symposium on Intelligent Control
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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